There are numerous generic data warehousing solutions in the marketplace, including software from SAS™, Oracle™ and others. Those solutions could be adapted to ware-housing of patient demographic and clinical data, but have not generally been utilized in that manner because of the vast complexities and incompatibilities that characterizes the current medical system.
One data warehousing solution that is specific to health care is the Common Health Framework (CHF) developed by the Connecting For Health, a consortium sponsored by the Merkle and Robert Wood Johnson Foundations. Their 2005 report is published at http://www.connectingfor_health.org/assets/reports/linking_report—2—2005.pdf, which along with and any other referenced patents and applications are incorporated herein by reference in their entirety. Where a definition or use of a term in a reference, which is incorporated by reference herein is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
According to the Framework, a central store contains only (a) patient identification information and (b) links to records in local databases. The idea is that the local databases can best maintain security and confidentiality of their own data, but queries can still be run against the central store for individual patients. Unfortunately, use of patient identification information as the sole portals through which one can access data means that the system provides only very limited and very difficult access to epidemiological data. For example, anyone wanting to run a correlation of particular AIDS treatments against side effects would first need to identify which patients have been diagnosed with AIDS. And there is no provision with the Framework for discovering which patients have that diagnosis. Thus, although the Framework can be used to great advantage to schedule individual patients appointments with clinicians, the Framework is unable to answer questions directed to how many (or what percentage of) patients having a given diagnosis missed their appointments during the last month. Consequently, there is still a need for a system that combines demographic, clinical, and other practice-related data from multiple independent data stores, and that can be conveniently mined for correlations by clinicians having only ordinary computer skills.